
#libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from matplotlib import pyplot
from sklearn.svm import SVC
from sklearn.inspection import permutation_importance
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import cross_validate
from sklearn.ensemble import StackingClassifier


train = pd.read_csv('titanic\\train.csv')
test = pd.read_csv('titanic\\test.csv')
gender = pd.read_csv('titanic\\gender_submission.csv')


train.head(5)


train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB


test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  418 non-null    int64  
 1   Pclass       418 non-null    int64  
 2   Name         418 non-null    object 
 3   Sex          418 non-null    object 
 4   Age          332 non-null    float64
 5   SibSp        418 non-null    int64  
 6   Parch        418 non-null    int64  
 7   Ticket       418 non-null    object 
 8   Fare         417 non-null    float64
 9   Cabin        91 non-null     object 
 10  Embarked     418 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 36.0+ KB


#How different is the survival ratio for males/females?
total = len(train)
no_male = train["Sex"].value_counts()["male"]
no_females = train["Sex"].value_counts()["female"]
survived_male = train[train.Sex.str.match("male")]["Survived"].value_counts()[1]
survived_females= train[train.Sex.str.match("female")]["Survived"].value_counts()[1]

print(f"Out of {no_male} males, {survived_male} survived. Their survivability rate is {survived_male/no_male*100}%.")
print(f"Out of {no_females} females, {survived_females} survived. Their survivability rate is {survived_females/no_females*100}%.")

#PLOTTING
fig, ax = plt.subplots(figsize= (10,10*0.618))
height = [survived_male/no_male*100, survived_females/no_females*100]
bars = ('Male', 'Female')
x_pos = np.arange(len(bars))
 
# Create bars and choose color
plt.bar(x_pos, height, color = ['blue','cyan'])

# Add title and axis names
plt.title('Survival Rate is considerably higher for women than men')
plt.xlabel('Gender')
plt.ylabel('Survival Rate %')
plt.xticks(x_pos, bars)

ax.set_ylim([0, 100]) #gives better perspective of percentages
 
# Show graph
plt.show()

Out of 577 males, 109 survived. Their survivability rate is 18.890814558058924%.
Out of 314 females, 233 survived. Their survivability rate is 74.20382165605095%.


# Passenger distribution and survival based on age?
#Not using imputed values
#Data
survived = train[train['Survived'] == 1]['Age']
died = train[train['Survived'] == 0]['Age']

#plot
fig, ax = plt.subplots(figsize = (10,10*0.618))

ax.hist([survived, died], bins=8, stacked=True, density=False, edgecolor='black')
ax.legend(("survived","died"))

plt.title('Survivors by Age Group')
plt.xlabel('Age')
plt.ylabel('Count')

Text(0, 0.5, 'Count')


#Passenger survival based on fare?
#Data
survived = train[train['Survived'] == 1]['Fare']
died = train[train['Survived'] == 0]['Fare']

#plot
fig, ax = plt.subplots(2, figsize = (10,10))

ax[0].hist([survived, died], bins=20, stacked=True, density=False, edgecolor='black')
ax[0].legend(("survived","died"))
ax[0].set_title('Survivors by Fare Group (skewness makes it hard to see survivability in high fare groups)')
ax[0].set_xlabel('Fare')
ax[0].set_ylabel('Count')

ax[1].hist([survived, died], log = True, bins=20, density=False, edgecolor='black')
ax[1].legend(("survived","died"))
ax[1].set_title('Survivors by Fare Group using log scale and not stacked')
ax[1].set_xlabel('Fare')
ax[1].set_ylabel('Count')

Text(0, 0.5, 'Count')


#Passenger survival based on Passenger class?
train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived')


#Do lone passengers have the same survivability as those accompanied?
train.loc[(train['SibSp'] > 0)|(train['Parch'] > 0), 'Company'] = 1
train.loc[(train['SibSp'] == 0)&(train['Parch'] == 0), 'Company'] = 0

no_alone = train["Company"].value_counts()[0]
no_accompanied = train["Company"].value_counts()[1]
survived_alone = train.loc[(train["Company"] == 1)]["Survived"].value_counts()[0]
survived_accompanied = train.loc[(train["Company"] == 1)]["Survived"].value_counts()[1]

print(f"Out of {no_alone} alone, {survived_alone} survived. Their survivability rate is {survived_alone/no_alone*100}%.")
print(f"Out of {no_accompanied} accompanied, {survived_accompanied} survived. Their survivability rate is {survived_accompanied/no_accompanied*100}%.")

Out of 537 alone, 175 survived. Their survivability rate is 32.588454376163874%.
Out of 354 accompanied, 179 survived. Their survivability rate is 50.56497175141242%.


#Do titles correlate with survivability?

#creating df with titles from names
train['Title'] = train['Name'].str.extract(' ([A-Za-z]+)\.', expand=False)

train['Title'].value_counts()

Mr          517
Miss        182
Mrs         125
Master       40
Dr            7
Rev           6
Col           2
Major         2
Mlle          2
Capt          1
Lady          1
Don           1
Mme           1
Ms            1
Countess      1
Sir           1
Jonkheer      1
Name: Title, dtype: int64


pd.crosstab(train['Title'], train['Survived'])


#Important note, to include gender in the correlation matrix, we change the 'Sex' column to 1 for males and 0 for females
#matrix only includes numerical features
train['Sex'].replace(['male','female'], [1,0], inplace = True)
test['Sex'].replace(['male','female'], [1,0], inplace = True)


#Correlation Matrix
fig = px.imshow(train[['Survived','Pclass','Age','SibSp', 'Parch', 'Sex', 'Fare', 'Company']].corr(), title='Correlation Matrix')
fig.show()


# Merge both data for preprocessing of data so we don't have to do it twice

total = pd.concat([test.assign(ind="test"), train.assign(ind="train")], ignore_index=True)
#added indicator column while concatenating the two dataframes, so we can later seperate them again


#Imputing missing age values with age average of similar passenger groups
total['Age'] = total['Age'].groupby(by=[total['Pclass'], total['Sex']])\
.apply(lambda x: x.fillna(x.mean())) #lambda function will allow to apply mean age of each subset

#Imputing missing Embarked with mode
total['Embarked'] = total['Embarked'].fillna(total['Embarked'].mode()[0])

#Imputing missing fare with median (since there are fare outliers, mean doesn't seem to be as optimal as median)
total['Fare'] = total['Fare'].fillna(total['Fare'].median())

#Dropping columns that have no use (PassengerId, Ticket) or very little data (Cabin)
total.drop(columns=['PassengerId', 'Ticket', 'Cabin'], axis=1, inplace = True)

#Adding column for wether a passenger is alone or not
total.loc[(total['SibSp'] > 0)|(total['Parch'] > 0), 'Company'] = 1
total.loc[(total['SibSp'] == 0)&(total['Parch'] == 0), 'Company'] = 0


total.drop(['SibSp', 'Parch'], axis = 1, inplace = True)

total.head(5)


categorical_columns = ['Pclass', 'Embarked']

for column in categorical_columns:
    encoder = OneHotEncoder(sparse=False, handle_unknown='ignore')
    encoded_total = pd.DataFrame(encoder.fit_transform(total[[column]]))

    # get OHE column names
    encoded_total.columns = encoder.get_feature_names([column])

    # One-hot encoding removed index; put it back
    encoded_total.index = total.index

    # Remove categorical columns (will replace with one-hot encoding)
    rem_total = total.drop(column, axis=1)

    # Add one-hot encoded columns to numerical features
    total = pd.concat([rem_total, encoded_total], axis=1)


total.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1309 entries, 0 to 1308
Data columns (total 14 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Name        1309 non-null   object 
 1   Sex         1309 non-null   int64  
 2   Age         1309 non-null   float64
 3   Fare        1309 non-null   float64
 4   ind         1309 non-null   object 
 5   Survived    891 non-null    float64
 6   Company     1309 non-null   float64
 7   Title       891 non-null    object 
 8   Pclass_1    1309 non-null   float64
 9   Pclass_2    1309 non-null   float64
 10  Pclass_3    1309 non-null   float64
 11  Embarked_C  1309 non-null   float64
 12  Embarked_Q  1309 non-null   float64
 13  Embarked_S  1309 non-null   float64
dtypes: float64(10), int64(1), object(3)
memory usage: 143.3+ KB


#We will drop the name column and replace it with the Title feature
#As seen from our EDA we have 4 very common titles and the remaining are rare ones
#we form 5 different categorical titles and one hot encode them to "feed the machine"
total['Title'] = total['Name'].str.extract(' ([A-Za-z]+)\.')
total['Title'].replace(['Lady', 'Countess','Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir',\
                        'Jonkheer', 'Dona', 'Mlle', 'Ms', 'Mme'], 'Rare', inplace = True)

title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}

total['Title'] = total['Title'].map(title_mapping)
total.loc[total['Sex'] == 1].Title.fillna(1, inplace = True) #Mr is the most common denomination for males
total.loc[total['Sex'] == 0].Title.fillna(2, inplace = True) #Miss is the most common denomination for females


#total.drop('Name', axis=1, inplace = True)

C:\Users\t_bor\Anaconda\Anaconda_NEW\lib\site-packages\pandas\core\series.py:4517: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\t_bor\Anaconda\Anaconda_NEW\lib\site-packages\pandas\core\series.py:4517: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


#drop name column
total.drop('Name', axis=1, inplace = True)


total.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1309 entries, 0 to 1308
Data columns (total 13 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Sex         1309 non-null   int64  
 1   Age         1309 non-null   float64
 2   Fare        1309 non-null   float64
 3   ind         1309 non-null   object 
 4   Survived    891 non-null    float64
 5   Company     1309 non-null   float64
 6   Title       1309 non-null   int64  
 7   Pclass_1    1309 non-null   float64
 8   Pclass_2    1309 non-null   float64
 9   Pclass_3    1309 non-null   float64
 10  Embarked_C  1309 non-null   float64
 11  Embarked_Q  1309 non-null   float64
 12  Embarked_S  1309 non-null   float64
dtypes: float64(10), int64(2), object(1)
memory usage: 133.1+ KB


#get PassengerId column for the submission csv before replacing test with the treated df
test_submission_labels = test['PassengerId']

#split data back into test and train:
test, train = total[total["ind"].eq("test")], total[total["ind"].eq("train")]


#drop ind
train.drop('ind', axis = 1, inplace = True)
test.drop('ind', axis = 1, inplace = True)

C:\Users\t_bor\Anaconda\Anaconda_NEW\lib\site-packages\pandas\core\frame.py:4163: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


#First we define what our target is: 'Survived'


X = train.drop(columns='Survived')
y = train['Survived']
# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42,
                                  test_size=0.25)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

(668, 11)
(223, 11)
(668,)
(223,)


#Hyperparameter tuning, chosing the best regularization for the model

param_grid = {'C': [0.005, 0.01, 0.05, 0.1, 0.5, 1]}
grid = GridSearchCV(LogisticRegression(solver='liblinear'), param_grid, cv=5)
grid.fit(X_train, y_train)

print("Best cross-validation score (f1): {:.2f}".format(grid.best_score_))
print("Best parameters: ", grid.best_params_)

Best cross-validation score (f1): 0.80
Best parameters:  {'C': 1}


#seeing the feature importance when using the best parameters for LR
best_LR =LogisticRegression(solver='liblinear', C = 0.01).fit(X_train, y_train)
importance = best_LR.coef_[0]
# summarize feature importance
for i,v in enumerate(importance):
    print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
fig, ax = plt.subplots(figsize=(15,5))
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.xticks(range(len(importance)), X_train)
pyplot.show()

Feature: 0, Score: -0.49627
Feature: 1, Score: -0.02482
Feature: 2, Score: 0.00895
Feature: 3, Score: -0.02964
Feature: 4, Score: 0.34101
Feature: 5, Score: 0.12766
Feature: 6, Score: 0.08107
Feature: 7, Score: -0.29350
Feature: 8, Score: 0.06582
Feature: 9, Score: -0.00761
Feature: 10, Score: -0.14298


param_grid = {
    'max_depth': [40, 80, 120],
    'n_estimators': [40, 80, 120, 160]
}

rf_clf = RandomForestClassifier()

grid_search = GridSearchCV(estimator = rf_clf, param_grid = param_grid, 
                          cv = 5, n_jobs = -1)

grid_search.fit(X_train, y_train)

print("Best cross-validation score (f1): {:.2f}".format(grid_search.best_score_))
print("Best params:\n{}\n".format(grid_search.best_params_))

Best cross-validation score (f1): 0.81
Best params:
{'max_depth': 40, 'n_estimators': 80}


#seeing the feature importance when using the best parameters for RF
best_rf = RandomForestClassifier(max_depth = 80, n_estimators = 40).fit(X_train, y_train)
importance = best_rf.feature_importances_
# summarize feature importance
for i,v in enumerate(importance):
    print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
fig, ax = plt.subplots(figsize=(15,5))
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.xticks(range(len(importance)), X_train)
pyplot.show()

Feature: 0, Score: 0.12644
Feature: 1, Score: 0.24461
Feature: 2, Score: 0.28329
Feature: 3, Score: 0.02449
Feature: 4, Score: 0.18885
Feature: 5, Score: 0.02596
Feature: 6, Score: 0.01548
Feature: 7, Score: 0.05451
Feature: 8, Score: 0.01236
Feature: 9, Score: 0.00923
Feature: 10, Score: 0.01478


param_grid = {'C': [0.1,1, 10, 100]}

clf_svm = SVC(gamma='auto')

grid = GridSearchCV(estimator = clf_svm, param_grid = param_grid, 
                          cv = 5)
grid.fit(X_train,y_train)

print("Best cross-validation score (f1): {:.2f}".format(grid.best_score_))
print("Best params:\n{}\n".format(grid.best_params_))

Best cross-validation score (f1): 0.71
Best params:
{'C': 1}


#seeing the feature importance when using the best parameters for svm
best_SVC = SVC(gamma='auto',C = 100).fit(X_train, y_train)

perm_importance = permutation_importance(best_SVC, X_test, y_test)

feature_names = X_train.columns
features = np.array(feature_names)

sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(features[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")

Text(0.5, 0, 'Permutation Importance')


param_grid = {'n_neighbors': [3, 5, 7, 9, 11]}
knn_grid = GridSearchCV(KNeighborsClassifier(), param_grid, cv= 5, scoring = 'f1')
knn_grid.fit(X_train, y_train)
knn_model = knn_grid.best_estimator_

print("Best cross-validation score (f1): {:.2f}".format(knn_grid.best_score_))
print("Best params:\n{}\n".format(knn_grid.best_params_))

Best cross-validation score (f1): 0.61
Best params:
{'n_neighbors': 3}


#seeing the feature importance when using the best parameters for KNN
best_knn = KNeighborsClassifier(n_neighbors = 9).fit(X_train, y_train)

# summarize feature importance
perm_importance = permutation_importance(best_knn, X_test, y_test)

feature_names = X_train.columns
features = np.array(feature_names)

sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(features[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")

Text(0.5, 0, 'Permutation Importance')


param_grid = {'n_estimators': range(20,110,10)}
GB_grid = GridSearchCV(GradientBoostingClassifier(), param_grid, cv= 5, scoring = 'f1')
GB_grid.fit(X_train, y_train)
GB_model = GB_grid.best_estimator_

print("Best cross-validation score (f1): {:.2f}".format(GB_grid.best_score_))
print("Best params:\n{}\n".format(GB_grid.best_params_))

Best cross-validation score (f1): 0.76
Best params:
{'n_estimators': 50}


#seeing the feature importance when using the best parameters for GB
# define the model
model = GradientBoostingClassifier(n_estimators = 50)
# fit the model
model.fit(X_train, y_train)
# get importance
importance = model.feature_importances_
# summarize feature importance
for i,v in enumerate(importance):
    print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
fig, ax = plt.subplots(figsize=(15,5))
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.xticks(range(len(importance)), X_train)
pyplot.show()

Feature: 0, Score: 0.02267
Feature: 1, Score: 0.07531
Feature: 2, Score: 0.17720
Feature: 3, Score: 0.00033
Feature: 4, Score: 0.55273
Feature: 5, Score: 0.00964
Feature: 6, Score: 0.00218
Feature: 7, Score: 0.14072
Feature: 8, Score: 0.00613
Feature: 9, Score: 0.00465
Feature: 10, Score: 0.00845


estimators = [('Logistic_optimized', LogisticRegression(solver='liblinear', C = 0.01)), 
              ('RForest_optimized', RandomForestClassifier(max_depth = 80, n_estimators = 80)),
              ('SVM_optimized', SVC(gamma='auto',C = 100)),
              ('KNN_optimized', KNeighborsClassifier(n_neighbors = 9)),
              ('GB_optimized', GradientBoostingClassifier(n_estimators = 50))]
    
stacking = StackingClassifier(estimators)
stacking.fit(X_train, y_train)

list_of_estimators = [('Logistic_optimized', LogisticRegression(solver='liblinear', C = 0.01)), 
                      ('RForest_optimized', RandomForestClassifier(max_depth = 80, n_estimators = 80)),
                      ('SVM_optimized', SVC(gamma='auto',C = 100)),
                      ('KNN_optimized', KNeighborsClassifier(n_neighbors = 9)),
                      ('GB_optimized', GradientBoostingClassifier(n_estimators = 50)),
                      ('stacking', stacking)]




for  label, model in list_of_estimators:
    cv_scores = cross_validate(model, X_train, y_train, cv=5, scoring=('f1','roc_auc'))
    print(f"F1-score: {cv_scores['test_f1'].mean():0.4f} (+/- {cv_scores['test_f1'].std():0.4f}) | ROC-AUC: {cv_scores['test_roc_auc'].mean():0.4f} (+/- {cv_scores['test_roc_auc'].std():0.4f}) [{label}]")

F1-score: 0.6337 (+/- 0.0588) | ROC-AUC: 0.8119 (+/- 0.0606) [Logistic_optimized]
F1-score: 0.7319 (+/- 0.0377) | ROC-AUC: 0.8596 (+/- 0.0295) [RForest_optimized]
F1-score: 0.5719 (+/- 0.0353) | ROC-AUC: 0.7543 (+/- 0.0325) [SVM_optimized]
F1-score: 0.5768 (+/- 0.0586) | ROC-AUC: 0.7371 (+/- 0.0356) [KNN_optimized]
F1-score: 0.7582 (+/- 0.0473) | ROC-AUC: 0.8615 (+/- 0.0332) [GB_optimized]
F1-score: 0.7445 (+/- 0.0429) | ROC-AUC: 0.8633 (+/- 0.0326) [stacking]


#preparing
X_test = test.drop(columns='Survived')


X_test.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 418 entries, 0 to 417
Data columns (total 11 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Sex         418 non-null    int64  
 1   Age         418 non-null    float64
 2   Fare        418 non-null    float64
 3   Company     418 non-null    float64
 4   Title       418 non-null    int64  
 5   Pclass_1    418 non-null    float64
 6   Pclass_2    418 non-null    float64
 7   Pclass_3    418 non-null    float64
 8   Embarked_C  418 non-null    float64
 9   Embarked_Q  418 non-null    float64
 10  Embarked_S  418 non-null    float64
dtypes: float64(9), int64(2)
memory usage: 39.2 KB


stacking.fit(X_train, y_train)


y_pred = stacking.predict(X_test)


y_pred.astype(int)
#we need the results in integer type

array([0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1,
       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,
       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,
       0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1])


submission = pd.DataFrame({
            "PassengerId": test_submission_labels,
            "Survived": y_pred.astype(int)
    })
submission.to_csv('../submission.csv', index=False)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

	Pclass	Survived
2	3	0.242363
1	2	0.472826
0	1	0.629630

	Pclass	Name	Sex	Age	Fare	Embarked	ind	Survived	Company	Title
0	3	Kelly, Mr. James	1	34.5	7.8292	Q	test	NaN	0.0	NaN
1	3	Wilkes, Mrs. James (Ellen Needs)	0	47.0	7.0000	S	test	NaN	1.0	NaN
2	2	Myles, Mr. Thomas Francis	1	62.0	9.6875	Q	test	NaN	0.0	NaN
3	3	Wirz, Mr. Albert	1	27.0	8.6625	S	test	NaN	0.0	NaN
4	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	0	22.0	12.2875	S	test	NaN	1.0	NaN

The Challenge¶

Brief Introduction¶

Table of Contents¶

EDA ¶

Data Preprocessing ¶

ML models ¶

logistics regression¶

Random Forest¶

Support Vector Machine¶

KNN¶

Gradient Boosting¶

Stacking ¶

Predicting ¶

Survived	0	1
Title
Capt	1	0
Col	1	1
Countess	0	1
Don	1	0
Dr	4	3
Jonkheer	1	0
Lady	0	1
Major	1	1
Master	17	23
Miss	55	127
Mlle	0	2
Mme	0	1
Mr	436	81
Mrs	26	99
Ms	0	1
Rev	6	0
Sir	0	1